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Artificial Intelligence Applications in Reservoir Engineering: A Status Check

Author

Listed:
  • Turgay Ertekin

    (John and Willie Leone Family Department of Energy and Mineral Engineering, The Pennsylvania State University, University Park, PA 16802, USA)

  • Qian Sun

    (Petroleum Recovery Research Center, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA)

Abstract

This article provides a comprehensive review of the state-of-art in the area of artificial intelligence applications to solve reservoir engineering problems. Research works including proxy model development, artificial-intelligence-assisted history-matching, project design, and optimization, etc. are presented to demonstrate the robustness of the intelligence systems. The successes of the developments prove the advantages of the AI approaches in terms of high computational efficacy and strong learning capabilities. Thus, the implementation of intelligence models enables reservoir engineers to accomplish many challenging and time-intensive works more effectively. However, it is not yet astute to completely replace the conventional reservoir engineering models with intelligent systems, since the defects of the technology cannot be ignored. The trend of research and industrial practices of reservoir engineering area would be establishing a hand-shaking protocol between the conventional modeling and the intelligent systems. Taking advantages of both methods, more robust solutions could be obtained with significantly less computational overheads.

Suggested Citation

  • Turgay Ertekin & Qian Sun, 2019. "Artificial Intelligence Applications in Reservoir Engineering: A Status Check," Energies, MDPI, vol. 12(15), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2897-:d:252292
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    References listed on IDEAS

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    1. Ampomah, W. & Balch, R.S. & Cather, M. & Will, R. & Gunda, D. & Dai, Z. & Soltanian, M.R., 2017. "Optimum design of CO2 storage and oil recovery under geological uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 80-92.
    2. Kim, Youngmin & Jang, Hochang & Kim, Junggyun & Lee, Jeonghwan, 2017. "Prediction of storage efficiency on CO2 sequestration in deep saline aquifers using artificial neural network," Applied Energy, Elsevier, vol. 185(P1), pages 916-928.
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    Cited by:

    1. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.
    2. Suryeom Jo & Changhyup Park & Dong-Woo Ryu & Seongin Ahn, 2021. "Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO 2 Geological Sequestration," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Deli Jia & Jiqun Zhang & Yanchun Li & Li Wu & Meixia Qiao, 2023. "Recent Development of Smart Field Deployment for Mature Waterflood Reservoirs," Sustainability, MDPI, vol. 15(1), pages 1-22, January.
    4. Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
    5. Dongmei Zhang & Yuyang Zhang & Bohou Jiang & Xinwei Jiang & Zhijiang Kang, 2020. "Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching," Energies, MDPI, vol. 13(17), pages 1-15, August.

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